The commercial deployment of automated vehicles (AVs) is around the corner. With the development of automation technology, automobile and IT companies have started to test automated vehicles. Waymo, an automated driving technology development company, has recently opened the self-driving service to the public. The advancement in this emerging mobility option also drives transportation reasearchers and urban planners to conduct automated vehicle-related research, especially to gain insights on the impact of automated vehicles (AVs) in order to inform policymaking. However, the variation with urban form, the heterogeneity of mode choice, and the impacts at disaggregated levels lead to the dynamics of the impacts of AVs, which not comprehensively understood yet. Therefore, this dissertation extends existing knowledge base by understanding the dynamics of the impacts from three perspectives: (1) examining the role of urban form in the performance of SAV systems; (2) exploring the heterogeneity of AV mode choices across regions; and (3) investigating the distribution of energy consumption in the era of AVs.
To examine the first aspect, Shared AV (SAV) systems are simulated for 286 cities and the simulation outcomes are regressed on urban form variables that measure density, diversity, and design. It is suggested that the compact development, a multi-core city pattern, high level of diversity, as well as more pedestrian-oriented networks can promote the performance of SAVs measured using service efficiency, trip pooling success rate, and extra VMT generation.
The AV mode choice behaviors of private conventional vehicle (PCV) users in Seattle and Knasas City metropolitan areas are examined using an interpretable machine learning framework based on an AV mode choice survey. It is suggested that attitudes and trip and mode-specific attributes are the most predictive. Positive attitudes can promote the adoption of PAVs. Longer PAV in-vehicle time encourages the residents to keep the PCVs. Longer walking distance promotes the usage of SAVs. In addition, the effects of in-vehicle time and walking distance vary across the two examined regions due to distinct urban form, transportation infrustructure and cultural backgrounds. Kansas City residents can tolerate shorter walking distance before switching to SAV choices due to the car-oriented environment while Seattle residents are more sensitive to in-vehicle travel time because of the local congestion levels.
The final part of the dissertation examines the demand for energy of AVs at disaggregated levels incorporating heterogeneity of AV mode choices. A three-step framework is employed including the prediction of mode choice, the determination of vehicle trajectories, and the estimation of the demand for energy. It is suggested that the AV scenario can generate -0.36% to 2.91% extra emissions and consume 2.9% more energy if gasoline is used. The revealed distribution of traffic volume suggests that the demand for charging is concentrated around the downtown areas and on highways if AVs consume electricity. In summary, the dissertation demonstrates that there is a dynamics with regard to the impacts and performance of AVs across regions due to various urban form, infrastructure and cultural environment, and the spatial heterogeneity within cities. / Doctor of Philosophy / Automated vehicles (AVs) have been a hot topic in recent years especially after various IT and automobile companies announced their plans for making AVs. Waymo, an automated driving technology development company, has recently opened the self-driving service to the public. Automated vehicles, which are defined as being able to self-drive, self-park, and automate routing, provide potentials for new business models such as privately owned automated vehicles (PAVs) that serve trips within households, shared AVs (SAVs) that offer door-to-door service to the public who request service using app-based platforms, and SAVs with pool where multiple passengers may be pooled together when the vehicles do not detour much if sequentially picking up and dropping off passengers. Therefore, AVs can transform the transportation system especially by reducing vehicle ownership and increasing travel distance. To plan for a sustainable future, it is important to gain an understanding of the impacts of AVs under various scenarios. Thus, a wealth of case studies explore the system performance of SAVs such as served trips per SAV per day. However, the impacts of AVs are not static and tend to vary across cities, depend on heterogeneous mode choices within regions, and may not be evenly distributed within a city. Therefore, this dissertation fills the research gaps by (1) investigating how urban features such as density may influence the system performance of SAVs; (2) exploring heterogeneity of key factors that influence the decisions about using AVs across regions; and (3) examining the distribution of the demand for energy in the era of AVs.
The first study in the dissertation simulates the SAVs that serve trips within 286 cities and examines the relationship between the system performance of SAVs and city features such as density, diversity, and design. The system performance of SAVs is evaluated using served trips per SAV per day, percent of pooled trips that allow ridesharing, and percent of extra Vehicle Miles Traveled (VMT) compared to the VMT requested by the served trips. The results suggest that compact diverse development patterns and pedestrian-oriented networks can promote the performance of SAVs.
The second study uses an interpretable machine learning framework to understand the heterogeneous mode choice behaviors of private car users in the era of AVs in two regions. The framework uses an AV mode choice survey, where respondents are asked to take mode choice experiments given attributes about the trips, to train machine learning models. Accumulated Local Effects (ALE) plots are used to analyze the model results. ALE outputs the accumulated change of the probability of choosing specific modes within small intervals across the range of the variable of interest. It is suggested that attitudes and trip-specific attributes such as in-vehicle time are the most important determinants. Positive attitudes, longer trips, and longer walking distance can promote the adoption of AV modes. In addition, the effects of in-vehicle time and walking distance vary across the two examined regions due to distinct urban form, transportation infrastructure, and cultural backgrounds. Kansas City residents can tolerate shorter walking distance before switching to SAV choices due to the car-oriented environment while Seattle residents are more sensitive to in-vehicle travel time because of the local congestion levels.
The final part of the dissertation examines the demand for energy of AVs at disaggregated levels incorporating heterogeneity of AV mode choices. A three-step framework is employed including the prediction of mode choice, the determination of vehicle trajectories, and the estimation of the demand for energy. It is suggested that the AV scenario can generate -0.36% to 2.91% of extra emissions and consume 2.9% more energy compared to a business as usual (BAU) scenario if gasoline is used. The revealed distribution of traffic volume suggests that the demand for charging is concentrated around the downtown areas and on highways if AVs consume electricity. In summary, the dissertation demonstrates that there is a dynamics with regard to the impacts and performance of AVs across regions due to various urban form, infrastructure and cultural environment, and the spatial heterogeneity within cities.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/118004 |
| Date | 24 August 2021 |
| Creators | Wang, Kaidi |
| Contributors | Public Administration/Public Affairs, Buehler, Ralph, Chen, T. Donna, Lim, Theodore Chao, Hall, Ralph P., Zhang, Wenwen |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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